Overview

Dataset statistics

Number of variables17
Number of observations9471
Missing cells37353
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory136.0 B

Variable types

Categorical2
Numeric13
Unsupported2

Warnings

Date has a high cardinality: 391 distinct values High cardinality
CO(GT) is highly correlated with PT08.S1(CO) and 8 other fieldsHigh correlation
PT08.S1(CO) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NMHC(GT) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
C6H6(GT) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S2(NMHC) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NOx(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S3(NOx) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NO2(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S4(NO2) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S5(O3) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
T is highly correlated with PT08.S4(NO2) and 2 other fieldsHigh correlation
RH is highly correlated with THigh correlation
AH is highly correlated with PT08.S4(NO2) and 1 other fieldsHigh correlation
CO(GT) is highly correlated with PT08.S1(CO) and 8 other fieldsHigh correlation
PT08.S1(CO) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NMHC(GT) is highly correlated with CO(GT) and 9 other fieldsHigh correlation
C6H6(GT) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S2(NMHC) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NOx(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S3(NOx) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
NO2(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S4(NO2) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S5(O3) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
T is highly correlated with NMHC(GT) and 3 other fieldsHigh correlation
RH is highly correlated with THigh correlation
AH is highly correlated with PT08.S4(NO2) and 1 other fieldsHigh correlation
CO(GT) is highly correlated with PT08.S1(CO) and 7 other fieldsHigh correlation
PT08.S1(CO) is highly correlated with CO(GT) and 6 other fieldsHigh correlation
NMHC(GT) is highly correlated with CO(GT) and 8 other fieldsHigh correlation
C6H6(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S2(NMHC) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
NOx(GT) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S3(NOx) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
NO2(GT) is highly correlated with CO(GT) and 4 other fieldsHigh correlation
PT08.S4(NO2) is highly correlated with NMHC(GT) and 2 other fieldsHigh correlation
PT08.S5(O3) is highly correlated with CO(GT) and 7 other fieldsHigh correlation
T is highly correlated with AHHigh correlation
AH is highly correlated with THigh correlation
Time is highly correlated with C6H6(GT) and 5 other fieldsHigh correlation
PT08.S3(NOx) is highly correlated with PT08.S5(O3) and 8 other fieldsHigh correlation
PT08.S5(O3) is highly correlated with PT08.S3(NOx) and 8 other fieldsHigh correlation
NOx(GT) is highly correlated with PT08.S3(NOx) and 7 other fieldsHigh correlation
C6H6(GT) is highly correlated with Time and 9 other fieldsHigh correlation
RH is highly correlated with THigh correlation
PT08.S4(NO2) is highly correlated with PT08.S3(NOx) and 8 other fieldsHigh correlation
CO(GT) is highly correlated with Time and 9 other fieldsHigh correlation
PT08.S1(CO) is highly correlated with Time and 9 other fieldsHigh correlation
T is highly correlated with RH and 2 other fieldsHigh correlation
AH is highly correlated with PT08.S4(NO2) and 1 other fieldsHigh correlation
PT08.S2(NMHC) is highly correlated with Time and 9 other fieldsHigh correlation
NO2(GT) is highly correlated with Time and 8 other fieldsHigh correlation
NMHC(GT) is highly correlated with Time and 9 other fieldsHigh correlation
Date has 114 (1.2%) missing values Missing
Time has 114 (1.2%) missing values Missing
CO(GT) has 1797 (19.0%) missing values Missing
PT08.S1(CO) has 480 (5.1%) missing values Missing
NMHC(GT) has 8557 (90.3%) missing values Missing
C6H6(GT) has 480 (5.1%) missing values Missing
PT08.S2(NMHC) has 480 (5.1%) missing values Missing
NOx(GT) has 1753 (18.5%) missing values Missing
PT08.S3(NOx) has 480 (5.1%) missing values Missing
NO2(GT) has 1756 (18.5%) missing values Missing
PT08.S4(NO2) has 480 (5.1%) missing values Missing
PT08.S5(O3) has 480 (5.1%) missing values Missing
T has 480 (5.1%) missing values Missing
RH has 480 (5.1%) missing values Missing
AH has 480 (5.1%) missing values Missing
Unnamed: 15 has 9471 (100.0%) missing values Missing
Unnamed: 16 has 9471 (100.0%) missing values Missing
Date is uniformly distributed Uniform
Time is uniformly distributed Uniform
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-07-04 21:53:51.268218
Analysis finished2021-07-04 21:54:28.552104
Duration37.28 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct391
Distinct (%)4.2%
Missing114
Missing (%)1.2%
Memory size74.1 KiB
03/03/2005
 
24
07/02/2005
 
24
18/05/2004
 
24
20/12/2004
 
24
19/09/2004
 
24
Other values (386)
9237 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters93570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10/03/2004
2nd row10/03/2004
3rd row10/03/2004
4th row10/03/2004
5th row10/03/2004

Common Values

ValueCountFrequency (%)
03/03/200524
 
0.3%
07/02/200524
 
0.3%
18/05/200424
 
0.3%
20/12/200424
 
0.3%
19/09/200424
 
0.3%
17/08/200424
 
0.3%
03/06/200424
 
0.3%
31/07/200424
 
0.3%
24/05/200424
 
0.3%
11/01/200524
 
0.3%
Other values (381)9117
96.3%
(Missing)114
 
1.2%

Length

2021-07-04T23:54:29.107828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25/07/200424
 
0.3%
15/02/200524
 
0.3%
23/11/200424
 
0.3%
07/02/200524
 
0.3%
18/05/200424
 
0.3%
20/12/200424
 
0.3%
19/09/200424
 
0.3%
17/08/200424
 
0.3%
03/06/200424
 
0.3%
31/07/200424
 
0.3%
Other values (381)9117
97.4%

Most occurring characters

ValueCountFrequency (%)
030180
32.3%
/18714
20.0%
214805
15.8%
48844
 
9.5%
17902
 
8.4%
53903
 
4.2%
32670
 
2.9%
71656
 
1.8%
81656
 
1.8%
61632
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number74856
80.0%
Other Punctuation18714
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030180
40.3%
214805
19.8%
48844
 
11.8%
17902
 
10.6%
53903
 
5.2%
32670
 
3.6%
71656
 
2.2%
81656
 
2.2%
61632
 
2.2%
91608
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/18714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common93570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030180
32.3%
/18714
20.0%
214805
15.8%
48844
 
9.5%
17902
 
8.4%
53903
 
4.2%
32670
 
2.9%
71656
 
1.8%
81656
 
1.8%
61632
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII93570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030180
32.3%
/18714
20.0%
214805
15.8%
48844
 
9.5%
17902
 
8.4%
53903
 
4.2%
32670
 
2.9%
71656
 
1.8%
81656
 
1.8%
61632
 
1.7%

Time
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct24
Distinct (%)0.3%
Missing114
Missing (%)1.2%
Memory size74.1 KiB
14.00.00
 
390
13.00.00
 
390
20.00.00
 
390
19.00.00
 
390
18.00.00
 
390
Other values (19)
7407 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74856
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
14.00.00390
 
4.1%
13.00.00390
 
4.1%
20.00.00390
 
4.1%
19.00.00390
 
4.1%
18.00.00390
 
4.1%
05.00.00390
 
4.1%
04.00.00390
 
4.1%
09.00.00390
 
4.1%
10.00.00390
 
4.1%
12.00.00390
 
4.1%
Other values (14)5457
57.6%

Length

2021-07-04T23:54:29.701339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13.00.00390
 
4.2%
23.00.00390
 
4.2%
20.00.00390
 
4.2%
19.00.00390
 
4.2%
18.00.00390
 
4.2%
05.00.00390
 
4.2%
04.00.00390
 
4.2%
09.00.00390
 
4.2%
10.00.00390
 
4.2%
12.00.00390
 
4.2%
Other values (14)5457
58.3%

Most occurring characters

ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number56142
75.0%
Other Punctuation18714
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
042498
75.7%
15067
 
9.0%
22730
 
4.9%
31170
 
2.1%
8780
 
1.4%
9780
 
1.4%
4780
 
1.4%
5779
 
1.4%
6779
 
1.4%
7779
 
1.4%
Other Punctuation
ValueCountFrequency (%)
.18714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII74856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

CO(GT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct96
Distinct (%)1.3%
Missing1797
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.152749544
Minimum0.1
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:30.153638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.1
median1.8
Q32.9
95-th percentile4.9
Maximum11.9
Range11.8
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.453252036
Coefficient of variation (CV)0.675067864
Kurtosis2.667779368
Mean2.152749544
Median Absolute Deviation (MAD)0.8
Skewness1.369752778
Sum16520.2
Variance2.111941481
MonotonicityNot monotonic
2021-07-04T23:54:30.541274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1305
 
3.2%
1.4279
 
2.9%
1.6275
 
2.9%
1.5273
 
2.9%
1.1262
 
2.8%
0.7260
 
2.7%
1.7258
 
2.7%
1.3253
 
2.7%
0.8251
 
2.7%
0.9248
 
2.6%
Other values (86)5010
52.9%
(Missing)1797
 
19.0%
ValueCountFrequency (%)
0.133
 
0.3%
0.245
 
0.5%
0.398
 
1.0%
0.4160
1.7%
0.5217
2.3%
0.6244
2.6%
0.7260
2.7%
0.8251
2.7%
0.9248
2.6%
1305
3.2%
ValueCountFrequency (%)
11.91
< 0.1%
11.51
< 0.1%
10.22
< 0.1%
10.11
< 0.1%
9.91
< 0.1%
9.51
< 0.1%
9.41
< 0.1%
9.31
< 0.1%
9.21
< 0.1%
9.12
< 0.1%

PT08.S1(CO)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1041
Distinct (%)11.6%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1099.833166
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:30.760512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile810.5
Q1937
median1063
Q31231
95-th percentile1508
Maximum2040
Range1393
Interquartile range (IQR)294

Descriptive statistics

Standard deviation217.0800373
Coefficient of variation (CV)0.1973754237
Kurtosis0.3351286502
Mean1099.833166
Median Absolute Deviation (MAD)142
Skewness0.7559073724
Sum9888600
Variance47123.74258
MonotonicityNot monotonic
2021-07-04T23:54:30.983191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97330
 
0.3%
110028
 
0.3%
93826
 
0.3%
98826
 
0.3%
92526
 
0.3%
96926
 
0.3%
98725
 
0.3%
98425
 
0.3%
105325
 
0.3%
97025
 
0.3%
Other values (1031)8729
92.2%
(Missing)480
 
5.1%
ValueCountFrequency (%)
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
6832
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%

NMHC(GT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct429
Distinct (%)46.9%
Missing8557
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean218.8118162
Minimum7
Maximum1189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:31.207252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile28.65
Q167
median150
Q3297
95-th percentile661.4
Maximum1189
Range1182
Interquartile range (IQR)230

Descriptive statistics

Standard deviation204.4599213
Coefficient of variation (CV)0.9344098724
Kurtosis2.270289034
Mean218.8118162
Median Absolute Deviation (MAD)94
Skewness1.557017103
Sum199994
Variance41803.8594
MonotonicityNot monotonic
2021-07-04T23:54:31.432431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6614
 
0.1%
299
 
0.1%
409
 
0.1%
888
 
0.1%
938
 
0.1%
847
 
0.1%
557
 
0.1%
957
 
0.1%
577
 
0.1%
607
 
0.1%
Other values (419)831
 
8.8%
(Missing)8557
90.3%
ValueCountFrequency (%)
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
< 0.1%
161
 
< 0.1%
174
< 0.1%
182
< 0.1%
192
< 0.1%
ValueCountFrequency (%)
11891
< 0.1%
11291
< 0.1%
10841
< 0.1%
10421
< 0.1%
9741
< 0.1%
9261
< 0.1%
8991
< 0.1%
8801
< 0.1%
8721
< 0.1%
8401
< 0.1%

C6H6(GT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct407
Distinct (%)4.5%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean10.08310533
Minimum0.1
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:31.643219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.7
Q14.4
median8.2
Q314
95-th percentile24.65
Maximum63.7
Range63.6
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.449819698
Coefficient of variation (CV)0.7388418008
Kurtosis2.488705886
Mean10.08310533
Median Absolute Deviation (MAD)4.4
Skewness1.36153227
Sum90657.2
Variance55.49981354
MonotonicityNot monotonic
2021-07-04T23:54:31.868888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.684
 
0.9%
2.882
 
0.9%
3.879
 
0.8%
478
 
0.8%
3.177
 
0.8%
376
 
0.8%
2.575
 
0.8%
2.973
 
0.8%
5.472
 
0.8%
671
 
0.7%
Other values (397)8224
86.8%
(Missing)480
 
5.1%
ValueCountFrequency (%)
0.12
 
< 0.1%
0.28
 
0.1%
0.310
 
0.1%
0.414
0.1%
0.520
0.2%
0.623
0.2%
0.731
0.3%
0.825
0.3%
0.925
0.3%
130
0.3%
ValueCountFrequency (%)
63.71
< 0.1%
52.11
< 0.1%
50.81
< 0.1%
50.71
< 0.1%
50.61
< 0.1%
49.51
< 0.1%
49.41
< 0.1%
48.21
< 0.1%
47.71
< 0.1%
47.51
< 0.1%

PT08.S2(NMHC)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1245
Distinct (%)13.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean939.1533756
Minimum383
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:32.063479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum383
5-th percentile562
Q1734.5
median909
Q31116
95-th percentile1420
Maximum2214
Range1831
Interquartile range (IQR)381.5

Descriptive statistics

Standard deviation266.8314286
Coefficient of variation (CV)0.2841191179
Kurtosis0.06324387318
Mean939.1533756
Median Absolute Deviation (MAD)188
Skewness0.56156598
Sum8443928
Variance71199.01129
MonotonicityNot monotonic
2021-07-04T23:54:32.266320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85325
 
0.3%
80023
 
0.2%
85923
 
0.2%
88023
 
0.2%
98522
 
0.2%
76921
 
0.2%
85021
 
0.2%
77621
 
0.2%
78321
 
0.2%
82820
 
0.2%
Other values (1235)8771
92.6%
(Missing)480
 
5.1%
ValueCountFrequency (%)
3832
< 0.1%
3871
< 0.1%
3881
< 0.1%
3902
< 0.1%
3971
< 0.1%
3991
< 0.1%
4022
< 0.1%
4072
< 0.1%
4081
< 0.1%
4091
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

NOx(GT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct925
Distinct (%)12.0%
Missing1753
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean246.8967349
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:32.488143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38
Q198
median180
Q3326
95-th percentile693
Maximum1479
Range1477
Interquartile range (IQR)228

Descriptive statistics

Standard deviation212.9791681
Coefficient of variation (CV)0.8626244822
Kurtosis3.40213437
Mean246.8967349
Median Absolute Deviation (MAD)100
Skewness1.715780799
Sum1905549
Variance45360.12605
MonotonicityNot monotonic
2021-07-04T23:54:32.727343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8941
 
0.4%
6537
 
0.4%
9336
 
0.4%
4136
 
0.4%
12236
 
0.4%
9535
 
0.4%
18035
 
0.4%
13235
 
0.4%
12034
 
0.4%
5134
 
0.4%
Other values (915)7359
77.7%
(Missing)1753
 
18.5%
ValueCountFrequency (%)
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
< 0.1%
114
< 0.1%
124
< 0.1%
134
< 0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13101
< 0.1%
13011
< 0.1%
12901
< 0.1%
12531
< 0.1%
12471
< 0.1%

PT08.S3(NOx)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1221
Distinct (%)13.6%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean835.4936047
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:32.952269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile483
Q1658
median806
Q3969.5
95-th percentile1291
Maximum2683
Range2361
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation256.81732
Coefficient of variation (CV)0.3073839447
Kurtosis2.677558895
Mean835.4936047
Median Absolute Deviation (MAD)155
Skewness1.101729235
Sum7511923
Variance65955.13586
MonotonicityNot monotonic
2021-07-04T23:54:33.164910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76725
 
0.3%
84625
 
0.3%
73325
 
0.3%
76523
 
0.2%
87623
 
0.2%
72022
 
0.2%
68522
 
0.2%
80022
 
0.2%
89122
 
0.2%
81622
 
0.2%
Other values (1211)8760
92.5%
(Missing)480
 
5.1%
ValueCountFrequency (%)
3221
< 0.1%
3252
< 0.1%
3281
< 0.1%
3302
< 0.1%
3341
< 0.1%
3351
< 0.1%
3402
< 0.1%
3411
< 0.1%
3451
< 0.1%
3461
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23311
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20811
< 0.1%

NO2(GT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct283
Distinct (%)3.7%
Missing1756
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean113.0912508
Minimum2
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:33.365722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile43
Q178
median109
Q3142
95-th percentile200.3
Maximum340
Range338
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.37010778
Coefficient of variation (CV)0.4277086639
Kurtosis0.4650321247
Mean113.0912508
Median Absolute Deviation (MAD)32
Skewness0.6217143134
Sum872499
Variance2339.667327
MonotonicityNot monotonic
2021-07-04T23:54:33.562922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9778
 
0.8%
11977
 
0.8%
11777
 
0.8%
10175
 
0.8%
9575
 
0.8%
11475
 
0.8%
11074
 
0.8%
11573
 
0.8%
11672
 
0.8%
10772
 
0.8%
Other values (273)6967
73.6%
(Missing)1756
 
18.5%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
145
0.1%
ValueCountFrequency (%)
3401
< 0.1%
3331
< 0.1%
3261
< 0.1%
3221
< 0.1%
3121
< 0.1%
3101
< 0.1%
3091
< 0.1%
3061
< 0.1%
3011
< 0.1%
2961
< 0.1%

PT08.S4(NO2)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1603
Distinct (%)17.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1456.264598
Minimum551
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:33.780717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile883
Q11227
median1463
Q31674
95-th percentile2029
Maximum2775
Range2224
Interquartile range (IQR)447

Descriptive statistics

Standard deviation346.2067935
Coefficient of variation (CV)0.2377361875
Kurtosis0.07801862433
Mean1456.264598
Median Absolute Deviation (MAD)221
Skewness0.2053885254
Sum13093275
Variance119859.1439
MonotonicityNot monotonic
2021-07-04T23:54:33.992109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148824
 
0.3%
158022
 
0.2%
153921
 
0.2%
146720
 
0.2%
163819
 
0.2%
149018
 
0.2%
141818
 
0.2%
147317
 
0.2%
132117
 
0.2%
143517
 
0.2%
Other values (1593)8798
92.9%
(Missing)480
 
5.1%
ValueCountFrequency (%)
5511
< 0.1%
5591
< 0.1%
5611
< 0.1%
5791
< 0.1%
6011
< 0.1%
6021
< 0.1%
6051
< 0.1%
6211
< 0.1%
6371
< 0.1%
6401
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26841
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26621
< 0.1%
26432
< 0.1%
26412
< 0.1%

PT08.S5(O3)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1743
Distinct (%)19.4%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1022.906128
Minimum221
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:34.726688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile461
Q1731.5
median963
Q31273.5
95-th percentile1761.5
Maximum2523
Range2302
Interquartile range (IQR)542

Descriptive statistics

Standard deviation398.4842877
Coefficient of variation (CV)0.3895609545
Kurtosis0.07861233923
Mean1022.906128
Median Absolute Deviation (MAD)261
Skewness0.6278644976
Sum9196949
Variance158789.7276
MonotonicityNot monotonic
2021-07-04T23:54:34.981647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82520
 
0.2%
83620
 
0.2%
82619
 
0.2%
92618
 
0.2%
77717
 
0.2%
79917
 
0.2%
92316
 
0.2%
90516
 
0.2%
89116
 
0.2%
94916
 
0.2%
Other values (1733)8816
93.1%
(Missing)480
 
5.1%
ValueCountFrequency (%)
2211
< 0.1%
2251
< 0.1%
2271
< 0.1%
2321
< 0.1%
2521
< 0.1%
2531
< 0.1%
2571
< 0.1%
2612
< 0.1%
2621
< 0.1%
2631
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24941
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%

T
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct436
Distinct (%)4.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean18.31782894
Minimum-1.9
Maximum44.6
Zeros1
Zeros (%)< 0.1%
Negative13
Negative (%)0.1%
Memory size74.1 KiB
2021-07-04T23:54:35.173270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9
5-th percentile4.6
Q111.8
median17.8
Q324.4
95-th percentile34.5
Maximum44.6
Range46.5
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation8.832115732
Coefficient of variation (CV)0.4821595267
Kurtosis-0.4562738166
Mean18.31782894
Median Absolute Deviation (MAD)6.3
Skewness0.3093567921
Sum164695.6
Variance78.0062683
MonotonicityNot monotonic
2021-07-04T23:54:35.417158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.857
 
0.6%
21.354
 
0.6%
20.251
 
0.5%
13.851
 
0.5%
1249
 
0.5%
15.649
 
0.5%
12.349
 
0.5%
19.848
 
0.5%
16.348
 
0.5%
14.647
 
0.5%
Other values (426)8488
89.6%
(Missing)480
 
5.1%
ValueCountFrequency (%)
-1.91
< 0.1%
-1.41
< 0.1%
-1.32
< 0.1%
-1.21
< 0.1%
-1.11
< 0.1%
-0.62
< 0.1%
-0.51
< 0.1%
-0.31
< 0.1%
-0.21
< 0.1%
-0.12
< 0.1%
ValueCountFrequency (%)
44.61
 
< 0.1%
44.31
 
< 0.1%
43.41
 
< 0.1%
43.11
 
< 0.1%
42.83
< 0.1%
42.71
 
< 0.1%
42.61
 
< 0.1%
42.51
 
< 0.1%
42.22
< 0.1%
422
< 0.1%

RH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct753
Distinct (%)8.4%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean49.23420087
Minimum9.2
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:35.650383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile20.3
Q135.8
median49.6
Q362.5
95-th percentile77.9
Maximum88.7
Range79.5
Interquartile range (IQR)26.7

Descriptive statistics

Standard deviation17.31689246
Coefficient of variation (CV)0.3517248611
Kurtosis-0.8183745211
Mean49.23420087
Median Absolute Deviation (MAD)13.3
Skewness-0.0379280099
Sum442664.7
Variance299.8747645
MonotonicityNot monotonic
2021-07-04T23:54:35.865281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.131
 
0.3%
47.830
 
0.3%
57.930
 
0.3%
45.927
 
0.3%
60.827
 
0.3%
50.826
 
0.3%
57.626
 
0.3%
47.626
 
0.3%
49.826
 
0.3%
50.926
 
0.3%
Other values (743)8716
92.0%
(Missing)480
 
5.1%
ValueCountFrequency (%)
9.22
< 0.1%
9.31
< 0.1%
9.61
< 0.1%
9.81
< 0.1%
9.92
< 0.1%
102
< 0.1%
10.21
< 0.1%
10.41
< 0.1%
10.71
< 0.1%
10.91
< 0.1%
ValueCountFrequency (%)
88.71
 
< 0.1%
87.21
 
< 0.1%
87.11
 
< 0.1%
871
 
< 0.1%
86.62
< 0.1%
86.52
< 0.1%
861
 
< 0.1%
85.73
< 0.1%
85.61
 
< 0.1%
85.51
 
< 0.1%

AH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6683
Distinct (%)74.3%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1.025530275
Minimum0.1847
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2021-07-04T23:54:36.293627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1847
5-th percentile0.40085
Q10.7368
median0.9954
Q31.3137
95-th percentile1.7256
Maximum2.231
Range2.0463
Interquartile range (IQR)0.5769

Descriptive statistics

Standard deviation0.403812606
Coefficient of variation (CV)0.3937598099
Kurtosis-0.5600978405
Mean1.025530275
Median Absolute Deviation (MAD)0.2861
Skewness0.2513877555
Sum9220.5427
Variance0.1630646208
MonotonicityNot monotonic
2021-07-04T23:54:36.505894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11996
 
0.1%
0.83946
 
0.1%
0.74876
 
0.1%
0.96846
 
0.1%
0.97226
 
0.1%
0.87365
 
0.1%
0.92715
 
0.1%
0.66865
 
0.1%
0.83255
 
0.1%
1.05945
 
0.1%
Other values (6673)8936
94.4%
(Missing)480
 
5.1%
ValueCountFrequency (%)
0.18471
< 0.1%
0.18621
< 0.1%
0.1911
< 0.1%
0.19751
< 0.1%
0.19881
< 0.1%
0.20291
< 0.1%
0.20311
< 0.1%
0.20621
< 0.1%
0.20861
< 0.1%
0.21571
< 0.1%
ValueCountFrequency (%)
2.2311
< 0.1%
2.18061
< 0.1%
2.17661
< 0.1%
2.17191
< 0.1%
2.13951
< 0.1%
2.13621
< 0.1%
2.12471
< 0.1%
2.11951
< 0.1%
2.1171
< 0.1%
2.11641
< 0.1%

Unnamed: 15
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing9471
Missing (%)100.0%
Memory size74.1 KiB

Unnamed: 16
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing9471
Missing (%)100.0%
Memory size74.1 KiB

Interactions

2021-07-04T23:53:55.351377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:55.535911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:55.729841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:55.933542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.107829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.293847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.456814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.618686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.793211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:56.975265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:57.293350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:57.444676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:57.615711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:57.798611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:57.973291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:58.154418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:58.365273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:58.541739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:58.734579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:58.917586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.095161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.269963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.434746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.607440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.768776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:53:59.980086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:00.149716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:00.343540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:00.533408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:00.721774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:00.917444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:01.100939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:01.273915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:01.473330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:01.642917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:01.967763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:02.148038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:02.357878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:02.528528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:02.718772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:02.882409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.049086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.257615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.457765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.627660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.819178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:03.997147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:04.170688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:04.352478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:04.521274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:04.666265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:04.847449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.003705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.180398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.365115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.562901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.740993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:05.917529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.091709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.251863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.416249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.593133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.754854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:06.931944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:07.164436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:07.339882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:07.515095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:07.857830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.034512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.193429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.353430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.537459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.710369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:08.880387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.039248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.181123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.350602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.541653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.710018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:09.867460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.042356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.254466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.418578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.608817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.791090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:10.960192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.108785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.257907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.423446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.595924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.751805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:11.882847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.056707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.219909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.398488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.561472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.735511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:12.922133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.080905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.239480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.393929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.553918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.698453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:13.868476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:14.042913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:14.225395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:14.608133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:14.793712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:14.955014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.139156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.301205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.459954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.645241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.796077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:15.942199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.082106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.256076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.409820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.565761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.724633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:16.896238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.093485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.274556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.436574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.592245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.737600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:17.891005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.051860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.214294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.402875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.550223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.704098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:18.856545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.041812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.197762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.356371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.514678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.656205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.820742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:19.970284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.104441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.248707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.390852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.524567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.686719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:20.887423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:21.092279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:21.290189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:21.499890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:21.681498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:21.852635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:22.032638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:22.203327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:22.366042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:22.541110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:22.995451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:23.200099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:23.393640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:23.554965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:23.747839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:23.897551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:24.058824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:24.242672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:24.388724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:24.634543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:24.912594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:25.285460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:25.569718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-04T23:54:25.873451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-04T23:54:36.705043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-04T23:54:37.433213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-04T23:54:37.812956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-04T23:54:38.134645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-07-04T23:54:26.915283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-04T23:54:27.469002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-04T23:54:27.923731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-04T23:54:28.306559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHUnnamed: 15Unnamed: 16
010/03/200418.00.002.61360.0150.011.91046.0166.01056.0113.01692.01268.013.648.90.7578NaNNaN
110/03/200419.00.002.01292.0112.09.4955.0103.01174.092.01559.0972.013.347.70.7255NaNNaN
210/03/200420.00.002.21402.088.09.0939.0131.01140.0114.01555.01074.011.954.00.7502NaNNaN
310/03/200421.00.002.21376.080.09.2948.0172.01092.0122.01584.01203.011.060.00.7867NaNNaN
410/03/200422.00.001.61272.051.06.5836.0131.01205.0116.01490.01110.011.259.60.7888NaNNaN
510/03/200423.00.001.21197.038.04.7750.089.01337.096.01393.0949.011.259.20.7848NaNNaN
611/03/200400.00.001.21185.031.03.6690.062.01462.077.01333.0733.011.356.80.7603NaNNaN
711/03/200401.00.001.01136.031.03.3672.062.01453.076.01333.0730.010.760.00.7702NaNNaN
811/03/200402.00.000.91094.024.02.3609.045.01579.060.01276.0620.010.759.70.7648NaNNaN
911/03/200403.00.000.61010.019.01.7561.0NaN1705.0NaN1235.0501.010.360.20.7517NaNNaN

Last rows

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHUnnamed: 15Unnamed: 16
9461NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9462NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9463NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9464NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9465NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9466NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9467NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9468NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9469NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9470NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN